If you recall the moments of deepest learning in your life, were they more “caught” or taught? That is, were they like inspiration that flowed from a mentor or more like theory conferred through teaching? If the former, how can higher education go beyond its focus on cognitive intelligence and cultivate more empathy, intrapersonal intelligence and resilience in students? The caught qualities are human skills that machines fail to replicate.
Contemporary anxiety over the relevance of universities’ role in society is putting this dichotomy under increasing scrutiny, and not simply because of the impact of generative AI on both education practice and the job market.
Is a computer science degree still relevant, for example? AI-assisted coding is improving rapidly, and no-code/low-code platforms are enabling more people to build software. The software-engineering job cuts we read about seem to follow. Yet, we need to caution against “AI washing”, where companies attribute job cuts solely to AI when cost-cutting or over-hiring may be the larger drivers. While AI is disrupting entry-level coding work to some extent, companies still need people who understand computer systems deeply. So, also emerging is the product-minded engineer who can make product decisions and implement AI tools with users in mind. AI reduces the premium on pure code writing but raises the demand for empathy-driven engineering.
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In AI and Humanity, an undergraduate course I teach in the College of Integrative Studies, students often share that their deepest lessons have gone beyond technical skills. One reflected that “being human is not about efficiency but about meaning, relationships and moral responsibility”. Another shared how what distinguishes humans from AI is our sense of empathy and responsibility towards others and the world around us.
The course includes a project where students design an AI agent to meet a societal challenge. Examples of target audiences include elderly people experiencing loneliness, caregivers suffering from burnout, families with dyslexic children, and individuals seeking mental health support. I ground students in the principle that the work does not start with technology. Instead, it begins with understanding the context and pain points of the target audience. As one student put it: “Empathy outperforms sheer engineering.” Another wrote, after interviewing domestic helpers, that “the most human thing is to simply listen and care”.
A case study points to the kind of AI literacy universities should cultivate, where students nurture not just the ability to build with AI, but the judgement to shape it for users and discover what realities it misses through an iterative process. Student researchers, working through the university’s role as a Reach Alliance partner, prototyped a conversational AI agent to support financial literacy among migrant workers in Singapore. Through focus groups and hands-on testing, migrant workers became co-designers of the tool, not just users. The team learned that generic advice such as “save $50 a month” did not fit the needs and circumstances of workers who were managing debt and irregular income. As one worker said: “After a long day on site, my eyes are tired. Voice is easier.”
Placing students in situations where they must bridge cultural and language barriers, and challenge their assumptions against lived realities develops their capacity for empathy. Rather than a sentiment, empathy becomes a design discipline. Cultural sensitivity will enable students to collaborate more effectively in a globalised world.
A recent assignment in AI and Humanity integrates AI literacy with intrapersonal intelligence. Students were tasked with designing a conversational agent to support self-discovery. I demonstrated the process through a real situation with my son. After feeling frustrated while helping him prepare for a major exam, I used GenAI to lead me through a reflective conversation to identify cognitive distortions and reframe the situation more positively. Students reported that the assignment helped them experience AI not as an oracle, but as a structured mirror for self-understanding. They were led to slow down their reasoning, identify emotions and surface hidden assumptions to reframe difficult experiences more constructively. Crucially, students did not conclude that AI could replace human support, but rather that it could scaffold intrapersonal intelligence.
Both empathy and intrapersonal intelligence go beyond cognitive intelligence towards whole-person formation, especially in an age when knowledge is increasingly accessible. Universities have long helped shape the hearts and minds of society. When they were first established, their curricula were rooted in disciplines concerned with reason, wisdom, moral life and what it means to be a human, as Singapore Management University president Lily Kong has written. They were not merely training grounds.
This is crucial if universities are to prepare students (and adults) for a 100-year life marked by multiple seasons of retooling. Technical knowledge will advance, and many skills will need to be relearned. But students who can understand themselves and others will possess a certain resilience to navigate a rapidly changing world. Empathy-driven engineering begins where technical competence meets self and other awareness, enabling us to be attentive to the communities and contexts that technology often overlooks.
Andrew Koh is a senior lecturer in computer science in the College of Integrative Studies at Singapore Management University.
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